Siamese Architecture: 
Finally, we justify the need for a two-branch Siamese network architecture.
Although our method can canonicalize even without this architecture, it makes it easier for category-level learning as observed in [1]}.
tab:ablation (c) compares our method on using a single branch architecture and a Siamese architecture.
Clearly, the Siamese architecture helps improve results. Enforcing different instances of the same category to align with each other reduces the Ground Truth Canonicalization error from \(3.49\)  to \(2.72\) . We also see a decrease in all the other metrics including Instance-Level Consistency.
